#Lagos download script
#lagosne_get(dest_folder = LAGOSNE:::lagos_path(),overwrite=T)
#Load in lagos
lagos <- lagosne_load()
## Warning in (function (version = NULL, fpath = NA) : LAGOSNE version unspecified,
## loading version: 1.087.3
#Grab the lake centroid info
lake_centers <- lagos$locus
# Make an sf object
spatial_lakes <- st_as_sf(lake_centers,coords=c('nhd_long','nhd_lat'),
crs=4326)
#Grab the water quality data
nutr <- lagos$epi_nutr
#Look at column names
#names(nutr)
clarity_only <- nutr %>%
select(lagoslakeid,sampledate,chla,doc,secchi) %>%
mutate(sampledate = as.character(sampledate) %>% ymd(.))
#Look at the number of rows of dataset
#nrow(clarity_only)
chla_secchi <- clarity_only %>%
filter(!is.na(chla),
!is.na(secchi))
# How many observatiosn did we lose?
# nrow(clarity_only) - nrow(chla_secchi)
# Keep only the lakes with at least 200 observations of secchi and chla
chla_secchi_200 <- chla_secchi %>%
group_by(lagoslakeid) %>%
mutate(count = n()) %>%
filter(count > 200)
spatial_200 <- inner_join(spatial_lakes,chla_secchi_200 %>%
distinct(lagoslakeid,.keep_all=T),
by='lagoslakeid')
### Take the mean chl_a and secchi by lake
mean_values_200 <- chla_secchi_200 %>%
# Take summary by lake id
group_by(lagoslakeid) %>%
# take mean chl_a per lake id
summarize(mean_chl = mean(chla,na.rm=T),
mean_secchi=mean(secchi,na.rm=T)) %>%
#Get rid of NAs
filter(!is.na(mean_chl),
!is.na(mean_secchi)) %>%
# Take the log base 10 of the mean_chl
mutate(log10_mean_chl = log10(mean_chl))
#Join datasets
mean_spatial <- inner_join(spatial_lakes,mean_values_200,
by='lagoslakeid')
#Make a map
mapview(mean_spatial,zcol='log10_mean_chl')
Secchi disk depth is negatively exponentially correlation with secchi disk depth. As chlorophyll increases, there becomes a point where very little light is able to penetrate the water beyond a short distance. At a certain point, it does not matter how much more additional chlorophyll is in the water, as the light cannot penetrate anyway.
#Your code here
ggplot(chla_secchi %>%
group_by(lagoslakeid) %>%
summarise(meanchla=mean(chla),
meansecchi=mean(secchi)),aes(meanchla,meansecchi)) +
geom_point()
# get count for each lake id
lake_centers <- lake_centers %>%
group_by(lagoslakeid,nhd_long,nhd_lat,state_zoneid) %>%
summarise(n=n())
## `summarise()` has grouped output by 'lagoslakeid', 'nhd_long', 'nhd_lat'. You can override using the `.groups` argument.
# join data to include state names
lake_states <- lagos$state
lake_statecenters<-left_join(lake_centers,lake_states,"state_zoneid")
# group by state and summarise to find total count
lake_obsn <- lake_statecenters %>%
group_by(state_name) %>%
summarise(n=sum(n)) %>%
arrange(desc(n)) %>%
drop_na()
states <- us_states() %>%
mutate(state_name=name)
# print table of counts
kable(lake_obsn)
| state_name | n |
|---|---|
| Minnesota | 29022 |
| Michigan | 15569 |
| Wisconsin | 13790 |
| New York | 11950 |
| Illinois | 11805 |
| Missouri | 9116 |
| Indiana | 7942 |
| Ohio | 6120 |
| Pennsylvania | 5922 |
| Maine | 5518 |
| Iowa | 4636 |
| Massachusetts | 3912 |
| New Jersey | 3333 |
| New Hampshire | 2544 |
| Connecticut | 2025 |
| Vermont | 1626 |
| Rhode Island | 618 |
# make map of counts by state
lake_statecenterboundaries <- left_join(lake_obsn,states,"state_name") %>%
drop_na()
lake_countmap <- st_as_sf(lake_statecenterboundaries)
mapview(lake_countmap,zcol='n')
Lakes that were further from urban areas tended to have higher secchi depth disks, indicating clearer water. This could be due to nutrient runoff from high population areas leading to high amounts of chla, and lower visibility in the water.
spatial_200 <- st_as_sf(left_join(chla_secchi_200,lake_centers,"lagoslakeid"),coords=c("nhd_long","nhd_lat"))
mapview(spatial_200,zcol='secchi')